Mikael Kuusela

Artificial Intelligence · 2010–2012

A researcher in the Science corpus: 2 papers; trusts 5 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 14 citations into it; #21,296 of 35,345 by trust here, #20,836 by citations
• Statistics and Probability — 8 citations into it
• Probabilistic & Bayesian Methods — 7 citations into it; #1,100 of 1,525 by trust here, #991 by citations
• Optimization & Training — 5 citations into it

Whom Mikael Kuusela trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Radford M. Neal
2. Shun-ichi Amari
3. Eleazar Eskin
4. Hagai Thomas Attias
5. Masa-aki Sato
6. Chris Fraley

Who trusts Mikael Kuusela overall

1. Michalis K. Titsias
2. Miguel L'azaro-Gredilla
3. David A. Knowles
4. Mohammad Emtiyaz Khan
5. Krikamol Muandet

Papers from Mikael Kuusela's perspective

Ordered by how strongly the researchers Mikael Kuusela trusts endorse each paper. Same corpus, a different order for every researcher.

1. An Introduction to Variational Methods for Graphical Models
2. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
3. Natural Gradient Works Efficiently in Learning
4. Riemann manifold Langevin and Hamiltonian Monte Carlo methods
5. The Geometry of Algorithms with Orthogonality Constraints
6. A Variational Bayesian Framework for Graphical Models
7. The Variational Gaussian Approximation Revisited
8. Online Model Selection Based on the Variational Bayes
9. Differential-geometrical methods in statistics
10. Natural Conjugate Gradient in Variational Inference

Their papers

• Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes (2010) — 114 citations
• Semi-supervised detection of collective anomalies with an application in high energy particle physics (2012) — 28 citations